Abstract
The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.
Highlights
The car rental industry plays a huge role globally
The demand data was estimated by four unconstrained estimation methods, and the three forecast models were used to predict the demand in the fourth presale lead time interval; 12 sets of demand data were obtained
To fully use the information of historical car rental data, a two-stage joint approach was proposed for predicting multitype car rental demand
Summary
The car rental industry plays a huge role globally. It can act as a lubricant for production and consumption to ease their mutual restraints. It can expand the automotive consumer market and evaluate the popularity of new cars before they come into the market. Because the public transportation system is limited by operating time, departure frequency, accessible range, comfort, privacy, and other conditions, car rental, with its outstanding advantages such as high flexibility, ease of use, and privacy, has shown considerable growth over the years. The demand for car rental services in the Asia-Pacific region has become the largest and fastest growing segment, especially in China and India, where there are high population density and rapidly growing demand
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